Petri Based Recurrent Fuzzy Neural Control for SY-II Remote Operated Vehicle

DOI

10.2991/iccasm.2012.88How to use a DOI?

Keywords

Remote Operated Vehicle, Petri Network(PN), Recurrent Fuzzy Neural Network(RFNN)

Abstract

Recurrent fuzzy neural network is widely applied in many areas because it combines the advantages of low level learning and high level reasoning. In considering the complicated factors and requirements for the Remote Operated Vehicle(ROV) control, petri network has been introduced to design a dynamic controller for underwater robot. It intends to reduce the computation burdens during network parameters learning. The gradient descent method has been used for online training. In order to guarantee its convergence, we have used the discrete Lyapunov function to determine its learning rate. The tank experiments have proved that the controller can adjust control quantity to reduce caculation and present strong advantages in the ROV robustness control.

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hai Huang
AU  - Lei Wan
AU  - Guocheng Zhang
AU  - Yongjie Pang
PY  - 2012/08
DA  - 2012/08
TI  - Petri Based Recurrent Fuzzy Neural Control for SY-II Remote Operated Vehicle
BT  - Proceedings of the 2012 International Conference on Computer Application and System Modeling (ICCASM 2012)
PB  - Atlantis Press
SP  - 349
EP  - 353
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasm.2012.88
DO  - 10.2991/iccasm.2012.88
ID  - Huang2012/08
ER  -